Concepts of Statistics

Concepts of Statistics provides a thorough and modern introduction to the principles, methods, and applications of probability and statistical inference. Designed for advanced undergraduates and graduate students, this book presents the essential ideas of statistics in a unified framework—balancing mathematical rigor with conceptual clarity and practical relevance.

The text begins with the foundations of probability, including sample spaces, random variables, expectation, and convergence theorems. These core ideas establish the groundwork for understanding uncertainty and modeling random phenomena. Building upon this foundation, the book advances into the domain of statistical inference, covering estimation, hypothesis testing, confidence intervals, and decision theory with a clear emphasis on intuition and structure.

A distinctive feature of Concepts of Statistics is its integration of classical and modern perspectives. Readers will find comprehensive treatments of both frequentist and Bayesian approaches, alongside advanced topics such as the bootstrap, simulation methods, and stochastic processes. The inclusion of chapters on causal inference, graphical models, nonparametric curve estimation, and classification methods connects traditional statistics with today’s data science and machine learning applications.

Throughout, the book emphasizes the connection between theory and application. Each chapter includes illustrative examples, bibliographic remarks, and well-designed exercises to reinforce understanding and encourage independent exploration.

This text serves as an invaluable resource for students and practitioners in statistics, mathematics, engineering, computer science, and the social sciences, as well as anyone seeking a deep and coherent understanding of statistical reasoning in the modern era.